# Robot Person Following Under Partial Occlusion

arXiv: https://arxiv.org/abs/2302.02121 · HTML: https://mlsanity.com/paper/2302.02121/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2023.

## Built on by (most cited first, 3 of 3)

- [CARPE-ID: Continuously Adaptable Re-identification for Personalized Robot Assistance](https://mlsanity.com/paper/2310.19413/) (2023)
- [Monocular Person Localization under Camera Ego-motion](https://mlsanity.com/paper/2503.02916/) (2025)
- [Navigating the Proximity-Safety Balance: Constraint Decomposition for Human Following in Pedestrian Crowds](https://mlsanity.com/paper/2608.10056/) (2026)

## Abstract (arXiv)

Robot person following (RPF) is a capability that supports many useful human-robot-interaction (HRI) applications. However, existing solutions to person following often assume full observation of the tracked person. As a consequence, they cannot track the person reliably under partial occlusion where the assumption of full observation is not satisfied. In this paper, we focus on the problem of robot person following under partial occlusion caused by a limited field of view of a monocular camera. Based on the key insight that it is possible to locate the target person when one or more of his/her joints are visible, we propose a method in which each visible joint contributes a location estimate of the followed person. Experiments on a public person-following dataset show that, even under partial occlusion, the proposed method can still locate the person more reliably than the existing SOTA methods. As well, the application of our method is demonstrated in real experiments on a mobile robot.